A Location-Specific Hyperdense Area Score Predicts Severe Mass Effect after Thrombectomy
摘要
In patients with anterior circulation large vessel occlusion stroke, hyperdense areas (HDAs) on post-thrombectomy computed tomography (CT) reflect blood–brain barrier injury and may herald malignant edema or hemorrhagic transformation. Both complications can progress to life-threatening mass effect, often necessitating surgical interventions such as decompressive craniectomy. We aimed to develop and validate a location-specific HDA score for predicting severe mass effect.
MethodsThis multicenter retrospective study analyzed 865 patients exhibiting HDAs on post-thrombectomy CT. The primary outcome was severe mass effect, defined as decompressive craniectomy or midline shift ≥ 5 mm on follow-up imaging. A location-specific HDA score was developed by expanding the ASPECTS template to include subarachnoid and ventricular compartments. Predictive models integrating imaging and clinical variables were constructed via LASSO regression and validated internally (bootstrapping) and externally.
ResultsThree HDA locations independently predicted the outcome: insula [adjusted odds ratio (aOR) = 2.02, 95% confidence interval (CI) 1.07–3.73, P = 0.030], subarachnoid space (aOR = 2.47, 95% CI 1.30–4.91, P = 0.005), and ventricles (aOR = 4.58, 95% CI 1.33–15.80, P = 0.016). The HDA score demonstrated a strong dose–response relationship with outcome risk, with scores ≤ 3 indicating low risk (< 10% probability) of severe mass effect and a high negative predictive value (88.8%). An integrated model (model 2) incorporating HDA features, admission blood glucose, National Institutes of Health Stroke Scale (NIHSS) and ASPECTS significantly outperformed a baseline clinical model (model 1) [area under the curve (AUC) 0.801 vs. 0.737, P < 0.001], showing high specificity (81.3%). Model performance was maintained in external validation (AUC 0.786) and in the contrast-staining subgroup (AUC 0.808).
ConclusionsA simple, location-specific HDA score, especially when integrated into a clinical prediction model, effectively identifies patients at high risk for severe mass effect after thrombectomy. This tool enables early risk stratification and may guide timely intensive care management.